Discussion of the Summit Theme. The following is the first draft of the Summit Theme and Description:

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Summit Theme and Description

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'''Ontology Summit 2020: Knowledge Graphs'''

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Ontology Summit 2020: Knowledge Graphs

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Knowledge graphs, closely related to ontologies and semantic networks, have emerged in the last few years to be an important semantic technology and research area. As structured representations of semantic knowledge that are stored in a graph, KGs are lightweight versions of semantic networks that scale to massive datasets such as the entire World Wide Web. Industry has devoted a great deal of effort to the development of knowledge graphs, and they are now critical to the functions of intelligent virtual assistants such as Siri and Alexa. Some of the research communities where KGs are relevant are Ontologies, Big Data, Linked Data, Open Knowledge Network, Artificial Intelligence, Deep Learning, and many others.

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Knowledge graphs have emerged in the last few years to be an important semantic

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The theme of the Summit is to examine KGs from a number of points of view ranging from low-level representation and storage techniques to high-level semantics, and from the vendors to the end users.

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technology and research area. While closely related to other semantic

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Rather than being split into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:

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technologies, KGs have some advantages for emerging applications. The theme of

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* Background

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the summit is to examine KGs from a number of points of view ranging from low

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** Definitions

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level representation and storage techniques to high level semantics, and from

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** History

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the vendors to the end users.

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* Standards

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Rather than divide the Summit into tracks, the Summit will cover a number of

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relevant areas with a mix of individual speaker sessions and panel discussion

Knowledge graphs have emerged in the last few years to be an important semantic technology and research area. While closely related to other semantic technologies, KGs have some advantages for emerging applications. The theme of the summit is to examine KGs from a number of points of view ranging from low level representation and storage techniques to high level semantics, and from the vendors to the end users.

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Rather than divide the Summit into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:

[12:22] Gary: From the NSF Convergence Accelerator (NSF 19-050) site: What is an Open Knowledge Network (OKN)?

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Knowledge networks are structured representations of semantic knowledge that are stored in a graph. Semantic knowledge graph development has largely been done in industry and is critical to the functions of intelligent virtual assistants such as Siri and Alexa. An Open Knowledge Network will similarly allow stored data to be located and its attributes and relationship to other data and to real-world objects and concepts to be understood at a semantic level; however, it will be an open, shared, public infrastructure that can drive innovation similar to the effects that development of the internet has had.

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[12:26] Gary: Sample NSF award for KG work that makes an argument for them: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528175&ActiveAwards=true&ExpiredAwards=true "...A real sea change in information search is coming! A broad range of new applications are emerging in intelligent policing, personal assistance, individualized healthcare, legal services, scientific literature search, and recently robotics. This project will serve these applications and make fundamental advances in querying heterogeneous knowledge graphs, which are ubiquitous. It is going to significantly ease query formulation and improve search quality/speed in these applications.

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Given the high data heterogeneity in knowledge graphs, writing structured queries that fully comply with data specification is extremely hard for ordinary users, while keyword queries can be too ambiguous to reflect user search intent. The situation becomes even worse when there are various representations for the same entity or relation. It is expected that a sophisticated query system shall be able to support different concept representations without forcing users to use very controlled vocabulary. It shall provide simple mechanisms to users so that they can quickly come up with a right query either explicitly or implicitly (e.g., via relevance feedback). This proposal is going to develop such system, make it user-friendly and scalable. The proposed research includes a plan to build a flexible query benchmark that is able to cope with heterogeneous, large-scale knowledge graphs, as well as user specified configurations and performance metrics. Benchmarks are indispensable for rapid development of database research. There were many successful examples of how robust and meaningful benchmarks can greatly expedite the development of a research area. The query benchmark proposed in this project is very needed. It is going to (1) provide a standardized way to fairly and comprehensively evaluate different knowledge graph query algorithms, (2) improve the understanding of the existing query engines, and (3) advance the area by getting researchers involved in the same play ground for building better, faster, and more intelligent methods.

Knowledge graphs, closely related to ontologies and semantic networks, have emerged in the last few years to be an important semantic technology and research area. As structured representations of semantic knowledge that are stored in a graph, KGs are lightweight versions of semantic networks that scale to massive datasets such as the entire World Wide Web. Industry has devoted a great deal of effort to the development of knowledge graphs, and they are now critical to the functions of intelligent virtual assistants such as Siri and Alexa. Some of the research communities where KGs are relevant are Ontologies, Big Data, Linked Data, Open Knowledge Network, Artificial Intelligence, Deep Learning, and many others.
&nbsp&nbsp&nbsp&nbsp(2A3)

The theme of the Summit is to examine KGs from a number of points of view ranging from low-level representation and storage techniques to high-level semantics, and from the vendors to the end users.
Rather than being split into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:
&nbsp&nbsp&nbsp&nbsp(2A4)

Knowledge graphs have emerged in the last few years to be an important semantic technology and research area. While closely related to other semantic technologies, KGs have some advantages for emerging applications. The theme of the summit is to examine KGs from a number of points of view ranging from low level representation and storage techniques to high level semantics, and from the vendors to the end users.
&nbsp&nbsp&nbsp&nbsp(2D3)

Rather than divide the Summit into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:
&nbsp&nbsp&nbsp&nbsp(2D4)

[12:17] Gary: We can add as part of the attraction -an interest in graph-based representation that are accessible to more developers provided a happy medium for
scalability, performance, and maintainability, especially for the applications involving big data.
&nbsp&nbsp&nbsp&nbsp(2D30)

[12:22] Gary: From the NSF Convergence Accelerator (NSF 19-050) site: What is an Open Knowledge Network (OKN)?
Knowledge networks are structured representations of semantic knowledge that are stored in a graph. Semantic knowledge graph development has largely been done in industry and is critical to the functions of intelligent virtual assistants such as Siri and Alexa. An Open Knowledge Network will similarly allow stored data to be located and its attributes and relationship to other data and to real-world objects and concepts to be understood at a semantic level; however, it will be an open, shared, public infrastructure that can drive innovation similar to the effects that development of the internet has had.
&nbsp&nbsp&nbsp&nbsp(2D31)

[12:26] Gary: Sample NSF award for KG work that makes an argument for them: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528175&ActiveAwards=true&ExpiredAwards=true "...A real sea change in information search is coming! A broad range of new applications are emerging in intelligent policing, personal assistance, individualized healthcare, legal services, scientific literature search, and recently robotics. This project will serve these applications and make fundamental advances in querying heterogeneous knowledge graphs, which are ubiquitous. It is going to significantly ease query formulation and improve search quality/speed in these applications.
&nbsp&nbsp&nbsp&nbsp(2D32)

Given the high data heterogeneity in knowledge graphs, writing structured queries that fully comply with data specification is extremely hard for ordinary users, while keyword queries can be too ambiguous to reflect user search intent. The situation becomes even worse when there are various representations for the same entity or relation. It is expected that a sophisticated query system shall be able to support different concept representations without forcing users to use very controlled vocabulary. It shall provide simple mechanisms to users so that they can quickly come up with a right query either explicitly or implicitly (e.g., via relevance feedback). This proposal is going to develop such system, make it user-friendly and scalable. The proposed research includes a plan to build a flexible query benchmark that is able to cope with heterogeneous, large-scale knowledge graphs, as well as user specified configurations and performance metrics. Benchmarks are indispensable for rapid development of database research. There were many successful examples of how robust and meaningful benchmarks can greatly expedite the development of a research area. The query benchmark proposed in this project is very needed. It is going to (1) provide a standardized way to fairly and comprehensively evaluate different knowledge graph query algorithms, (2) improve the understanding of the existing query engines, and (3) advance the area by getting researchers involved in the same play ground for building better, faster, and more intelligent methods.
&nbsp&nbsp&nbsp&nbsp(2D33)